Modelling the chlorophenol removal from wastewater via reverse osmosis process using a multilayer artificial neural network with genetic algorithm
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2020-02Keyword
Wastewater treatmentReverse osmosis process
Modelling
Chlorophenol removal
Artificial neural network
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© 2019 Elsevier Ltd. All rights reserved. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.Peer-Reviewed
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openAccess
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Show full item recordAbstract
Reverse Osmosis (RO) can be considered as one of the most widely used technologies used to abate the existence of highly toxic compounds from wastewater. In this paper, a multilayer artificial neural network (MLANN) with Genetic Algorithm (GA) have been considered to build a comprehensive mathematical model, which can be used to predict the performance of an individual RO process in term of chlorophenol removal from wastewater. The MLANN model has been validated against 70 observational experimental datasets collected from the open literature. The MLANN model predictions have outperformed the predictions of several structures developed for the same chlorophenol removal using RO process based on performance in terms of coefficient of correlation, coefficient determination (R2) and average error (AVE). In this respect, two structures (4-2-2-1) and (4-8-8-1) were also used to study the effect of a number of neurons in the hidden layers based on the difference between the measured and ANN predicted values. The model responses clearly confirm the successfulness of estimating the chlorophenol rejection for network structure 4-8-8-1 based on a wide range of the control variables. This also represents a high consistency between the ANN model predictions and the experimental data.Version
Accepted manuscriptCitation
Mohammad AT, Al-Obaidi MA, Hameed EM et al (2020) Modelling the chlorophenol removal from wastewater via reverse osmosis process using a multilayer artificial neural network with genetic algorithm. Journal Of Water Process Engineering. 33: 100993.Link to Version of Record
https://doi.org/10.1016/j.jwpe.2019.100993Type
Articleae974a485f413a2113503eed53cd6c53
https://doi.org/10.1016/j.jwpe.2019.100993